System and method for identification of individual animals based on images of the back

ABSTRACT

The present disclosure relates to a system and a method for identification of individual animals based on images, such as 3D-images, of the animals, especially of cattle and cows. When animals live in areas or enclosures where they freely move around, it can be complicated to identify the individual animal. The present disclosure relates to a method for determining the identity of an individual animal in a population of animals with known identity, the method comprising the steps of acquiring at least one image of the back of a preselected animal, extracting data from said at least one image relating to the anatomy of the back and/or topology of the back of the preselected animal, and comparing and/or matching said extracted data against reference data corresponding to the anatomy of the back and/or topology of the back of the animals with known identity, thereby identifying the preselected animal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/738,823, filed Dec. 21, 2017, which is the National Stage ofPCT/EP2016/065241, filed Jun. 30, 2016, which claims the benefit ofEuropean Application Number 15174783.9, filed Jul. 1, 2015, thedisclosures of which are incorporated herein by reference in theirentireties.

The present disclosure relates to a system and a method foridentification of individual animals based on images, such as 3D-images,of the animals, especially of cattle and cows.

BACKGROUND OF INVENTION

Identification of individuals of livestock animals such as pigs, cattleand cows is usually performed by systems such as non-electronicidentification tags e.g. ear notching, ear tags, number tags in neckchains and electronic identification where the most common includeelectronic ear tags, microchips, and electronic collars. Each of thesesystems has advantages and drawbacks and the systems cannot be usedsolely for identification of individuals in groups with simultaneouslyautomatic collection of other information relevant for the individualanimal.

When producing milk from cows up to 80% of the expenses are used forfeed to the cows. Optimization of the feed intake relative to the milkproduction and the health of the cow may reduce the expenses not onlyused for feed but also for medicine or veterinarian support. Cow healthand wellness can be increased by having the cows in loose-housing systemwhere the cows can move around and thus strengthen the bones andmuscles. In these loose-housing system it can be difficult to determinethe feed intake for each cow as an estimation of feed intake must becorrelated to the individual cow.

WO 95/28807 (‘Three-dimensional phenotypic measuring system foranimals’, Pheno Imaging Inc.) describes a three-dimensional phenotypicmeasuring system for animals such as dairy cows. The system uses a largenumber of modulated laser light beams from a laser camera to measureapproximately 100 points per square inch of the animal. Each laser beammeasures intensity, horizontal, vertical, and depth dimensions, and bycombining the measurements, the system composes a very accuratethree-dimensional image of the animal. The system calculates the desiredphenotypic measurements for conformation of the animal by combiningmeasurements of selected points on the animal. The system then storesthe measurements for each animal in a computer data base for later use.The system also stores a light intensity image of the animal's markingswhich is compared to other stored images. The system makes pictures ofside views of the animals and is used for grading the animals. Thesystem can scan the data bank for each new animal to ensure that thesame animal is not processed more than once.

EP 2027770 (‘Method and apparatus for the automatic grading of conditionof livestock’, Icerobotics Limited) describes a method of and apparatusfor grading a characteristic of an animal. The animal is guided to adetection area whereupon an image of the back of the animal is captured.The identity of the animal is furthermore established when the animal isin the detection area. The identity is determined by means of reading anidentification mark located on the animal. Analysis of the imageidentifies anatomical points and determines angles at these points. Theangles are then used to calculate a grading for a characteristic of theanimal. An embodiment is presented for automating the determination ofbody score condition in dairy cows using seven angles determined atthree anatomical points from an image over the back of the cow.

Hence, identification of an individual animal is easy if it is possibleto have access to the identification mark which is attached to eachanimal. But many animals live in a loose-housing system where access toeach animal's identification mark is not possible at any given time.Further, the animals may be located in an open-air field. In bothsituations it is impossible to monitor each individual animal if theidentification mark cannot be accessed.

SUMMARY OF INVENTION

If an individual animal in a loose-housing system cannot be monitoredconstantly or frequently it is virtually impossible to register the feedintake of each animal. The presently disclosed invention thereforerelates to a method for determining the identity of an individual animalfrom the natural appearance and/or topology of the back of the animal.The present inventors have realized that each animal has uniquecharacteristics associated with the natural configuration, appearance,topology and/or contours of the back of the animal. The inventors havefurthermore realized that these characteristics can be extracted fromone or more images showing at least a part of the back of an animal. Thefortunate result is that animals can be identified from an image of theback of said animal if a previous, and preferably substantially recentimage, exists of the same animal, by comparing these images, such as byextracting corresponding features of the images that can be compared. Byusing images of the back of the animals makes it possible to identifyand monitor animals from above, e.g. based on camera systems mounted inthe ceiling of a barn/stable or from an airborne camera system, e.g.

airborne by means of a drone. Airborne camera systems can furthermore beapplied for identifying and monitoring animals in an open-air field.

In one embodiment the presently disclosed method therefore comprises thesteps of:

-   -   Obtaining at least one image of at least a part of the back of        an animal, e.g. an un-identified animal, and    -   Extracting data from the at least one obtained image, the        extracted data, e.g. predefined characteristics, relating to the        natural appearance, anatomy, contour and/or topology of the back        of the animal.

When the image(s) has been analyzed and extracted data thereby obtainedthe animal can be identified if e.g. predefined characteristics in theimage matches predefined characteristics of a previous (reference) imageof the same animal. A correspondence between two or more images of thesame animal can therefore be established because the anatomy of the backof an animal is unique to each animal, at least in a herd or populationof animals with only a limited number of animals. The previous(reference) image may furthermore be associated with the identity of theanimal, e.g. with the identity of the animal corresponding to theidentification mark of the animal. Hence, once a correspondence isestablished between the identity of the animal, e.g. via theidentification mark, and one or more predefined anatomic characteristicsof the back of the animal, this animal can subsequently be uniquelyidentified solely by means of images showing (at least a part of) theback of said animal.

In a further embodiment the extracted data is compared with referencedata extracted from at least one reference image of a back of anidentified animal, where the information of the identity of theidentified animal may be connected to the at least one reference image.Further, based on the comparison, it can be determined whether theun-identified animal corresponds to the identified animal. The steps ofcomparing the extracted data with reference data and determining whetherthe un-identified animal corresponds to an identified animal, may berepeated for a plurality of reference images of a plurality ofidentified animals until a match is obtained and the un-identifiedanimal has been identified. The extracted data may also be matched orcompared against a database of predefined (anatomical) characteristics,the database e.g. comprising predefined characteristics of each animalin the population or herd of animals that need to be distinguished and aset of predefined characteristics may be associated with exactly oneanimal of known identity. Once a match between sets of predefinedcharacteristics is obtained the un-identified animal is identified.

The present disclosure further relates to a method for determining theidentity of an individual animal in a population of animals with knownidentity, the method comprising the steps of:

-   -   acquiring at least one image of the back of a preselected        animal, and    -   extracting data from said at least one image relating to the        anatomy, natural appearance and/or topology of the back of the        preselected animal, and    -   comparing and/or matching said extracted data against reference        data corresponding to the anatomy, natural appearance and/or        topology of the back of the animals with known identity, thereby        identifying the preselected animal.

The system and method as herein disclosed can therefore determine theindividual animal based on the anatomy of the back of an animal, wherebyit is possible to estimate the intake of e.g. roughage by combining theinvention described herein with the system for determining feedconsumption as described in e.g. WO 2014/166498 (‘System for determiningfeed consumption of at least one animal’, Viking Genetics FMBA) where animage system is used to assess the amount of feed consumed by eachidentified animal by determining the reduction of feed in subsequentimages of the feeding area in front of each identified animal.

With the presently disclosed identification method it might be feasiblethat animals do not need a visible identification mark because theanimals are distinguishable based on the back images. Hence, once imagesare initially acquired of the back of all animals, they can subsequentlybe distinguished from each other based on the different images of theback of each animal and thereby identified.

Comparing extracted data from at least one image with extracted datafrom a previous (reference) image may be performed by any methodpossible to compare data and may be based on any data directly extractedfrom the images or from any data calculated on the basis of the images.Vectors may be calculated, scores may be determined such as principalcomponents (PC scores) for a principal component analysis and these maybe included in the comparing process and/or used to perform furthercalculation such as a dot product and the comparing is then performedfrom the calculated product.

Animals may be any animal species, race or group and may e.g. beselected from the group of cattle, cows, dairy cows, bulls, calves,pigs, sows, boars, castrated males, piglets, horses, sheep, goats, deer.

Reference data may be extracted from at least one (reference) imageacquired of the back of each of the animals in the population ofanimals. A reference image of an animal may be obtained by concurrentlydetermining the identity of the animal by reading an identificationmarker attached to said animal.

Hence, at least one reference image of the back of an identified animalmay for example be obtained by

-   -   providing the identification number of an animal, hereby the        animal being an identified animal,    -   providing at least one image of the back of the identified        animal, and    -   storing in a database the identification number of the        identified animal together with the at least one image of the        back of the identified animal the image hereby being a reference        image.

The at least one reference image of the back of an identified animal maybe obtained frequently, such as each day, but may be determined due tothe type of animals to identify. Relatively short time span of e.g. oneor two days may be important when identifying dairy cows.

The method may be based on images and reference images which aretopographic images of the back of the animals, such images may beobtained as 3D images.

The present disclosure also relates to an animal identification systemfor determining the identity of an individual animal among a populationof animals with known identity, the system may comprise

-   -   an imaging system configured for acquiring at least one image of        the back of a preselected animal, and    -   a processing unit configured for        -   extracting data from said at least one image relating to the            anatomy, natural appearance and/or topology of the back of            the preselected animal, and        -   matching said extracted data to reference data corresponding            to the anatomy, natural appearance and/or topology of the            back of each of the animals with known identity, thereby            identifying the preselected animal.

The system may further comprise a reference imaging unit for providingone or more reference images of an animal in the population of animals,said reference imaging unit comprising

-   -   at least one identity determining device configured to determine        the identity of said animal, such as by reading at least one        identification marker attached to said animal, and    -   at least one camera configured to acquire at least one        (reference) image of the back of said animal.

The system may further be configured to associate the determinedidentity of the animal with said at least one image acquired by saidcamera(s) and optionally store said at least one image as a referenceimage.

Hence, the preselected animal may be seen as un-identified because atthe time of image acquisition the system may not know the animal'sidentity. On the other hand the identity of the preselected animal isnot unknown per se, because it has previously been identified andreference data, possibly comprising characteristics of the animal'sanatomy, exists such that the preselected animal can be automaticallyidentified shortly after image acquisition. The reference data may bebased/extracted from one or more previous images of the preselectedanimal.

The processing unit may be part of a computing device and images,extracted data, reference images, and/or reference data may be exchangedwith a database which may be part of the animal identification system orthe system may have access to the database. The imaging system maycomprise one or more cameras. The animal identification system may beconfigured such that at least some of said cameras are arranged suchthat they are located above the animals to be identified in order to beable to image the back of the animals. The cameras may be in a fixedlocation but may be configured such that the field of view can be variedin order to image different areas. The presently disclosed animalidentification system may also be part of an airborne system aspreviously indicated.

A further embodiment of the animal identification system relates to asystem for determining the identity of an individual animal from thenatural appearance and/or topology of the back of said animal, thesystem may comprise

-   -   at least one camera for obtaining at least one image of the back        of an un-identified animal,    -   at least one database or admission to at least one database for        storing data related to at least one reference image of the back        of an identified animal and for storing data related to at least        one image of the back of an un-identified animal,    -   data transmission means for transmitting data from said at least        one camera to said database, and    -   at least one processing means connected to said database, said        processing means being configured for comparing extracted data        from said at least one image from an un-identified animal with        extracted data from at least one reference image where said        extracted data is related to the natural appearance and/or        topology of the back of the animal and based on this comparing        determine whether said un-identified animal corresponds to said        identified animal.

Preferably the obtained images of the back of the animals are 3D imagesand which can be obtained by any suitable camera system capable ofproviding 3D images, such a system may be based on e.g. range cameras,stereo cameras, time-of-flight cameras.

The method and system may be used not only for determining the identityof animals but also for e.g. determining the amount of feed consumed byan animal. Images of feed located in front of an eating animal may beanalyzed by similar methods as described herein for animalidentification to determine the amount of feed consumption. Theinvention makes it possible to determine feed consumption of individualanimals and store such information in a database, e.g. in connectionwith that animal's file. Also grading conditions or health conditionsmay be monitored with the system described herein and such informationmay also be stored in the animal's file making it possible to follow ananimal's development and/or optimize its production, e.g. milkproduction, by controlling the type and amount of feed consumption.

The systems disclosed herein may be configured to carry out any of theherein disclosed methods.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 illustrates eating cows in a cowshed in which a system of thepresent invention is installed.

FIG. 2 illustrates examples of different pre-selected points at the backof a cow.

FIG. 3 illustrates examples of features established in respect of theback of an animal, here the back of a cow.

FIG. 4A illustrates the height profile along the backbone of a cow.

FIG. 4B illustrates the height profile along the backbone of anothercow.

FIG. 5 illustrates a Mesa Imaging 3D reconstruction of the part of a cowwith a height above 90 cm from floor level.

FIG. 6 illustrates the back of a cow.

FIG. 7 illustrates the back of the cow of FIG. 6 with indications ofsome data/features which can be used in the analysis.

FIG. 8 illustrates area determination based on rescaled data obtainedfrom the part of a cow with a height above 90 cm from the floor.

FIG. 9A illustrates a thickness profile for a first cow measured 90 cMabove floor level.

FIG. 9B illustrates a thickness profile for the first cow measured alongthe line indicated by step 3 in FIG. 7.

FIG. 9C illustrates a thickness profile for the first cow measured alongthe line indicated by step 4 in FIG. 7.

FIG. 9D illustrates a longitudinal height profile along the backbone ofthe first cow.

FIG. 10A illustrates a thickness profile for a second cow measured 90 cMabove floor level.

FIG. 10B illustrates a thickness profile for the second cow measuredalong the line indicated by step 3 in FIG. 7.

FIG. 10C illustrates a thickness profile for the second cow measuredalong the line indicated by step 4 in FIG. 7.

FIG. 10D illustrates a longitudinal height profile along the backbone ofthe second cow.

FIG. 11 illustrates a vertical height profile of a cow.

FIG. 12 illustrates determination of a cow based on neural network suchas a deep learning system.

DETAILED DESCRIPTION OF THE INVENTION

An aspect of the invention relates to a method for determining theidentity of an individual animal from the natural appearance and/ortopology of the back of the animal as described above. When comparingdata extracted from at least one image of an (un-identified) animal withreference data extracted from at least one reference image, the data tocompare is obtained from corresponding features of the back of theanimals. The data to compare is extracted from features of the back ofanimals. Such features are based on the natural appearance and/ortopology of the back of the animal. Natural features may include anyfeature described herein as well as any marks in the skin such asscratches, scars etc. Preferably natural features do not includepermanent ID-tags applied to the animal by human, such as brands oridentification numbers applied by e.g. freeze branding, hot branding ortattooing.

The identity of an animal may be an identification number, a name orcode used to uniquely identify the animal, e.g. in the population, in aregion, country and/or globally. An ‘identified animal’ is therefore ananimal with an identity.

An ‘un-identified animal’ as used herein means an animal in respect ofwhich at a certain point of time no identity is connected to an image ofthe back of the animal and where the identity may be an identificationnumber of the animal. An un-identified animal is preferably an animalbelonging to a population of identified animals, e.g. each animal havingan identification number, this population may be a herd of e.g. cows orcattle or other animals described elsewhere herein. When using themethod and system as described herein, animals may change status betweenidentified and un-identified animal and back again within a very shorttime. The change of status of an animal may occur when an animal walksthrough a corral or shed and at least one new image of the back of theanimal is obtained. When data extracted from this at least one image hasbeen compared with data extracted from at least one reference image anda match is found the animal changes status from un-identified toidentified. An un-identified animal may thus also be denoted as ananimal to be identified.

An image of an un-identified animal is preferably obtained at a locationwhere it is not easy or impossible to register an ID tag of the animalunambiguously simultaneously with obtaining the image. Such a locationmay be in a field where the distance from an electronic ID tag to anantenna capable of registering IDs is too large for registering and/or anon-electronic ID tag cannot be viewed by an imaging means due to toolong distance and/or the tags position at the animal makes it impossibleto view the ID tag. The location may also be where animals are too closeto each other to register an individual ID which for certain can beconnected to an image of the animals back taken substantially at thesame time where the animal ID is registered. Such a location may also bea field or a loose-housing system, e.g. a loose-housing system for cows,such as a feeding area for cows in loose-housing systems.

The term ‘the back of an animal’ as used herein as ‘back of anun-identified animal’ or ‘back of an identified animal’ is a referenceto the anatomical part of the animal containing the spinal column, i.e.the dorsum. Thus, the term ‘the back of an animal’ as used herein is notintended to refer to the hind or rear of the animal, e.g. the part ofthe cow comprising its hind legs, as might be viewed from one side offrom behind the animal. Thus the at least one image and the at least onereference image are obtained from above the animal e.g. directly fromabove or from an angle above the animal. Images and reference imagestaken from above an animal may together with the back also include thehead and neck of the animal and these parts of the animals may also beused to compare an image with at least one reference image.

The present invention is based on the realization that the back of ananimal can be used as a unique anatomical characteristic. Hence, byacquiring one or more images of at least a part of the back andextracting data relating to the anatomy and/or topology of the back, theanimal can be identified by comparing to previously referencedcharacteristics. An image of the back an animal as used herein shouldtherefore comprise sufficient information such that relevantcharacteristics of the anatomy and/or topology of the back can beextracted from the image. In one embodiment at least a part of thespinal column is therefore included in the image. In a furtherembodiment an image of the back of an animal includes the spinal columnfrom the tail head along and at least to the point where the neckbegins. The beginning of the neck (seen from the back towards the headof the animal) may be defined by a ‘neck point’ which is the locationbetween the body of the animal and the head where the body thickness isless than a predetermined part of the widest width of the animal, forcows and cattle the ‘neck point’ may be where the neck is less than 38%of the widest width of the animal. The ‘neck point’ for cows isillustrated in FIG. 7 as the area including the left end points of thecurves illustrated along the back of the cow. Preferably also theposition of at least one shoulder-blade (scapula) is included whenobtaining an image of the back of an animal.

An image of the back of an animal preferably also includes at least theupper 10, 15 or 20 cm of at least one side of the animal, where thisdistance is calculated from any highest point along the spinal columnand downward, hereby the spinal column and a virtual lower line e.g. 15cm below the spinal column would have similar contours (be parallel).For cows/cattle an image of the back should preferably include at leastthe spinal column from the tail head to the neck and at least 15 cmbelow the spinal column on at least one side of the cow/cattle.

When obtaining at least one image of the back of an animal the idealsituation is to obtain the at least one image substantially directlyabove the animal, where the image can include the spinal column and thearea on both sides of the spinal column which is visible from aboveHowever for practical reasons it may be unfeasible to use an imagingsystem where each animal, e.g. in a stable, can be imaged directly fromabove. In practical implementation (a part of) the area on one side ofthe spinal column can be partly or fully blocked by the higher lyingspinal column in the field of view in the image(s), for example if theimaging system is not located high enough relative to the correspondinganimals.

Hence, when obtaining at least one image of the back of an animal wherethe image is obtained from an angle such that it does not include datafrom both sides of the spinal column, or if data of a part of one sideof the spinal column is missing, then the missing data may be calculatedsuch that the corresponding data from one side of the spinal column ismirrored to the other side of the spinal column to obtain an entire setof data of the back of the animal. Such an ‘entire set of data’ shouldbe understood as the term ‘image’ as used herein i.e. an ‘image’ may bedata obtained from an image obtained without mirroring any data or itmay be data obtained from an image obtained with mirroring some data. Inpractice an image of an animal may be obtained including the spinalcolumn and the area on just one side e.g. the left side of the animal,this image may be turned into an ‘entire set of data’ by mirroring thedata from the left side to the right side of the animal before using theimage (i.e. the entire set of data) to determine the identification ofthe animal as described herein. Mirroring data from one side of the backof an animal to the other side of the back of the animal may beperformed for any images obtained, such as images obtained at an angleof less than ±90° where the starting position is the location of thelongitudinal direction of the spinal column.

The step of mirroring data may be performed when the processing of dataregisters missing information, such that the missing information may beobtained by mirroring the corresponding data from the other side of thespinal column.

Mirroring is not necessary if enough information is contained in theimage such that sufficient data relating to anatomical and/ortopological characteristics of the back can be extracted from the imagein order to identify the animal.

The data obtained from an image may also include data related to theneck and/or to the head. Such data may however be used for otherpurposes than for determining the identity of the animal, e.g. fordetermining the location of the nose. When determining the location ofthe nose this may correspond to the fact the animal is eating and fromwhere the animal is eating, such information may be correlated toinformation determining the feed intake. Thus by identifying the nose ofan eating animal this corresponds to identify the location of a virtualfeeding trough from where the feed intake may be determined.

The term “compare images” should be understood as comparing dataextracted from the images.

In a reference image of an animal the identity of the animal shown inthe image is known.

One or more reference images of the back of an animal, such as anidentified animal, may be obtained at least once a month, such as atleast every third week, e.g. at least every second week or at least oncea week. Preferable a reference image is obtained at least twice a week,such as at least three times a week e.g. at least four times a week,such as at least five times a week. Preferably at least one referenceimage of an animal is obtained at least every second day, morepreferable at least one reference image of an animal is obtained atleast once a day, such as twice a day, e.g. three times a day.

For determination of an interval between obtaining at least onereference images of the back of an animal, possible changes of thenatural appearance and/or topology of the back should be considered. Theinterval between obtaining subsequent reference images should be shortenough to register changes of the appearance and/or topology of the backfor the individual animal and still be capable of identifying the animalbased on images of the back. For dairy cattle the interval in timebetween obtaining reference images is preferably shorter than forfattening cattle. Also the purpose of identifying an un-identifiedanimal should be considered when determining a time interval betweenobtaining reference images. Such purposes are described elsewhere hereinand may be related to a request for information of e.g. physiologicalstatus, stature, health, fitness etc.

A reference image of an animal can be obtained at a location where it isknown that the animal has to pass at least one time a day if this is thedetermined interval between obtaining reference images. Such a locationcan be at the entry or exit from the milking area if the animal is in agroup of dairy cows. A location for obtaining a reference image may alsobe at a drinking trough, at a drive way, drink station or another placewhere the animal most likely will be or pass every day or frequently.

The suitable time and longest acceptable time i.e. the interval betweenobtaining two reference images of a single animal may also be determineddue to characteristics of the animal, these characteristics may be race,breed, age, matureness, health etc. The interval may also be determineddue to the purpose of controlling the animal and the purpose ofidentifying the animal. The purpose of controlling the animal can be forthe production of milk, meat, young (e.g. piglets) or semen or it may befor other purposes such as conservation or presentation in e.g. ZOOs oruse for competitions e.g. horse race and show jumping. Each purpose forkeeping the animal may affect the animal shape including the backappearance or back topology differently and with different speed. Ananimal kept for milk production may have a negative energy balance andis usually getting thinner rather quickly during the period with milkingand therefore a short interval between obtaining reference images may berecommended, whereas an animal kept for meat production althoughincreasing its size does not change appearance or topology of the backas fast as a dairy cow and for the animal kept for meat production itmay only be necessary to obtain a reference image once a month or onceevery second week. Other factors may also have an influence on theappearance of the appearance and/or topology of the back of the animalsuch as the health.

A reference image and/or reference data of an animal is an image of (theback of) and animal or data, e.g. anatomical characteristics,corresponding to the animal where the identity of the animal is known,i.e. if the image is stored in a database the identity of the animal isassociated/connected with the image and data associated with the imagecomprises information of the identity of the animal.

In an embodiment at least one reference image of the back of an animalis obtained by

-   -   providing the identification number of the animal, hereby the        animal being an identified animal and    -   acquiring at least one image of the back of the identified        animal.

The identification number of the identified animal and the at least oneimage of the back of the identified animal can subsequently be storedtogether in a database, the image hereby being a reference image. Datacan also be extracted from the image to provide reference data of theidentified animal and reference data can be stored, e.g. in a database.Storing the reference data only instead of the actual images is moreefficient in terms of storage space.

Providing the identification number of an animal and providing at leastone image of the back of this animal may be done simultaneously orshortly after each other in any order. Shortly may mean within less than60 seconds, such as less than 30 seconds, e.g. less than 15 seconds,e.g. less than 10 seconds, such as less than 5 seconds, e.g. less than 1second, such as less than 0.5 second.

When the identification number of the animal is obtained and at leastone image of the back of the same animal is obtained and these arestored together this is a reference image of an identified animal, i.e.the animal's identification and the appearance, anatomy and/ortopography of its back is known or may become known when obtaining andprocessing data from the at least one image and these data may be storedtogether with the animal-ID in a database. The identification number ofan animal may be obtained by any known method e.g. based on anelectronic tag, such as electronic ear tag, an electronic tag in acollar or a microchip beneath the skin. Also non-electronic tags arepossible.

When the identity of an animal is obtained e.g. by an identitydetermining device, this may trigger a system to providing at least oneimage of the back of this identified animal. The reference image of theback of an identified animal may also be obtained shortly after theidentification number of the identified animal has been provided. Areference image and/or the ID of the animal may also be obtainedmanually where the ID number is entered into a system by a human and/ora human may trigger a camera to obtain at least one image of the back ofan animal with the ID number that is or is to be entered into thesystem.

In principle any animal image, or extracted data thereof, acquired asdescribed herein may become a reference image, because once anidentification of the animal in the image is provided according to theherein disclosed method there is an association/connection between theimage of the animal and the identity of the animal in the image.

When a new animal enters a population e.g. when a new cow or cattlejoins a herd at least one reference image may be obtained of the back ofthis animal. The at least one reference image may initially beconsidered an image of an unknown animal and tested in the system tomake sure no match is obtained between this image and the referenceimages in the database. If a match is found between the at least oneimage of the new animal and the reference images in the databases thenumber of features used to compare images and reference images shouldpreferably be increased until no match is obtained based on the image ofthe new animal. Afterwards the at least one image of the new animal canbe considered a reference image or a group of reference images. For eachanimal a number of reference images may be stored. When comparing atleast one image of an un-identified animal with reference images, it maybe decided only to compare with the reference images obtained latest foreach identified animal, such reference images may be e.g. the latest 2,3, 4, 5, 6, 7, 8, 9 or 10 reference images obtained for each animal orit may be averages of data extracted from reference images obtained thelatest e.g. 2, 3, 4, 5, 6, 7, 8, 9 or 10 times the animal has beensubjected to recording reference images.

In practice each one of at least one image of an un-identified animalmay be compared to at least one reference image of a number of animals.An identity of an animal may be determined by comparing a number ofimages of the back of this animal with a number of reference images ofanimals e.g. in a herd and the identity may be determined to be thematch with reference images obtained most times. If e.g. 10 images of anun-identified animal are compared to reference images and 8 of theseimages match at least one reference image of animal A and the remaining2 images match at least one reference image of animal B theun-identified animal may be determined to be animal A.

The number of images of the back of an un-identified animal which shouldbe compared with at least one reference image of a number of identifiedanimals may be at least 5, such as at least 10, e.g. at least 15, suchas at least 20, e.g. at least 25, such as at least 30, e.g. at least 35,such as at least 40, e.g. at least 45, such as at least 50, e.g. atleast 75, such as at least 100. Preferably the number of images of theback of an un-identified animal which should be compared with at leastone reference image of a number of identified animals is about 5, suchas about 10, e.g. about 15, such as about 20, more preferably as about10, e.g. about 15.

The image and the reference image may be topographic images of the backof the animals, such that both are 3D images. 3D images may be turnedinto layers of 3D images, hereby the image and reference image each maybe multiple layers of 3D-images each including a number of pixelscorresponding to the size (length and width wise) of the animal and thenumber of layers corresponding to the height of the animal. Whendetermining the identity of an un-identified animal the at least oneobtained image is compared with the at least one reference image bycomparing data in respect of at least one feature obtained from the atleast one image with a data in respect of at least one correspondingfeature obtained from the at least one reference image.

The at least one feature used for comparing at least one image with atleast one reference image may be values of the area of multiple layersof said 3D-image. The at least one feature may also be values selectedfrom the group of: topographic profile of the animal, the height of theanimal, the broadness of the animal, contour line or height profilealong the backbone of the animal, the length of the back, contour plotsfor different heights of the animal, size of cavities, depth ofcavities, the distance between two pre-selected points or features atthe animal, angles between lines determined between pre-determinedpoints or features of the animal, vertical height profile(s) atdifferent pre-selected points. Examples of the use of data extractedfrom images are described in Example 2, one or more of these data typesmay be used together with any other data types mentioned herein as wellas with more types of data extracted directly from the images orcalculated from data extracted from the images and the type and numberof data may be determined due to the number of animals and due to theanimal species and/or race in a herd.

Height of the animal may be the average height of the contour line alongthe backbone or it may be the height at the legs e.g. the average heightat the legs or it may be the height at the tail head. The length of theback may be determined as the length in a height of 90% the total heightof the animal e.g. for an animal with maximum height of 165 cm thelength of the back is determined at the height of 148.5 cm. A broadnessof the animal may be determined as the broadness between twopre-selected points. A contour line length along the backbone may bedetermined as the distance from the neck to the tail head. A verticalheight profile may be determined along the length of the backbone. Whendetermining contour plots for different heights of an animal, the areaof the back of the animal at certain heights is determined e.g. % ofheight at 166-170 cm, % of height at 161-165 cm, % of height at 156-160cm, % of height at 151-155 cm, of height at 146-150 cm etc to obtain agroup of areas for the animal. The described height may be amended dueto the actual height of an animal to be identified of an identifiedanimal. Examples of contour plots are given in Example 2.

When comparing data from images to determine the identity of an animal,this may be performed by comparing ‘masks’ of the back of the animalwith corresponding ‘masks’ of animal backs in reference images. A ‘mask’may include the animal's back and optionally also the neck and the headof the animal. A ‘mask’ of an animal's back is data describing thetopology of the animal's back and may be visualized as shown in FIG. 5.

Pre-selected points can be selected from the group of right hip, lefthip, right shoulder, left shoulder, tail head, neck, (1) left forerib,(2) left short rib start, (3) left hook start, (4) left hook anteriormidpoint; (5) left hook, (6) left hook posterior midpoint, (7) left hookend, (8) left thurl, (9) left pin, (10) left tail head nadir, (11) lefttail head junction, (12) tail, (13) right tail head junction, (14) righttail head nadir, (15) right pin, (16) right thurl, (17) right hook end,(18) right hook posterior midpoint, (19) right hook, (20) right hookanterior midpoint, (21) right hook start, (22) right short rib start,and (23) right forerib. The indicated numbers correspond to numbers inFIG. 2. The location of these points and/or their height e.g. abovefloor level may by itself be data for comparison of images however, morepreferably these points are used for calculating distances to eachother, for calculating angles between different lines between differentpoints, for determining location of longitudinal and/or vertical heightprofiles etc.

The features to use when comparing at least one image with at least onereference image may be any feature which is measurable and/ordetectable. Preferably the feature is a natural characteristic of theanimal such as a part of the phenotype of the animal, although alsowounds and/or scars may be used as a feature. The feature is preferablynot a mark applied to the animal by human such as a brand e.g. an IDbrand. Phenotype features include the features mentioned above and canalso be skin colors, color pattern, location of cavities, depth ofcavities and/or areas of cavities.

When comparing the at least one feature or data obtained from at leastone image this may be performed as a sequential identification proceduresequentially comparing a single feature of an un-identified animal witha corresponding feature of identified animals.

A sequential identification procedure can be by comparing a firstfeature e.g. the animal height obtained from an image of anun-identified animal with a corresponding first feature of images ofidentified animals i.e. from reference images, hereby close in on theidentified animals fulfilling the feature (=a closed in firstpopulation), and afterwards proceed to a second feature e.g. length ofthe back of the un-identified animal which is compared to the secondfeature of identified animals of the closed in population furtherclosing in this population to a closed in second population. Thisprocedure is continued with other features until a match of theun-identified animal with a single identified animal is obtained. Thefinal match of the un-identified animal with a single identified animalindicates that the un-identified animal corresponds to the identifiedanimal and hereby the un-identified animal is identified.

Comparing of the image with the reference image may also be performed bycomparing feature vectors obtained from the at least one image withcorresponding feature vectors obtained from the at least one referenceimage. A feature vector may be based on at least two of the featuresdescribed herein.

When comparing the at least one feature or data obtained from at leastone image this may also be performed by calculation of a value for eachpicture where this value is determined from a number of data. The valuemay be a dot product between vectors e.g. as described in Example 2.

The at least one image and the reference image of the back of animalsmay be obtained within an angle of between 0 and 50 degree above theanimal, where 0 is in a direction straight above the central part of theback of the animal such as straight above the backbone of the animal.Preferably the angle is between 0 and 40°, more preferably between 0 and30°.

When obtaining at least one image and/or at least one reference imagewithin an angle different from 0, the system may automatically correlatefor the deformation within the images and/or the comparing of at leastone image may be performed with at least one reference image obtainedfrom substantially same angle measured according to any line drawnthrough the animal. Substantially same angle may be a deviation of ±5°,such as ±4° e.g. ±3°. Preferred is ±2° most preferred is a deviation of±1°.

The at least one reference image of the back of an un-identified animalis preferably obtained with only one animal present in an area coveredby a reference imaging unit providing at least one reference image ofthe back of the animal.

A triggering mechanism can be located close to the reference imagingunit. The triggering mechanism may be located such that when an animalis activating the triggering mechanism the mechanism is actuated andsends a signal to the reference imaging unit to collect at least oneimage of the back of the animal. For example, a detector could bemounted on a gate which is triggered when the cow contacts the gate.

The at least one image of the back of an un-identified animal may beobtained with one or more animals present in an area covered by animaging unit for obtaining images of the back of at least oneun-identified animal. The system is preferably capable of distinguishingdifferent animals from each other in one image i.e. when an image coversmore than one animal each of these animals can preferably be identified.

The method as described herein may be used for identifying any kind ofanimal. Preferable the animal is selected from the group of cattle,cows, dairy cows, bulls, calves, pigs, sows, boars, castrated male pigs,piglets, horses, sheep, goats, deer. The animal may also be one or moreanimals living in a ZOO, a park or in the nature. Such animals may beelephants, monkeys, giraffes, hippopotamus, rhinoceros, wolfs, foxes,bears, tigers, lions, cheetahs, pandas, leopards, tapirs, llamas,camels, reindeers, okapis, antelopes, gnus.

The method of identifying an animal can be used to check whether theidentified animal is still among the population or it may be dead. Themethod can also be used to further analysis as described herein such asto estimate the health or wellness of the animal or be combined withother methods to estimate the feed intake of the animal, such as asystem for determining feed consumption of at least one animal asdescribed in WO 2014/166498.

Registered health conditions may be used to evaluate differentconditions such as:

-   -   the physiological status of the animal, including body scoring        elements detectable in the images obtained from above the animal        i.e. from the back of the animal, the neck and the head,    -   the overall health status of the animal,    -   state of reproduction i.e. whether the animal such as a cow is        ready to be inseminated/fertilized; this may be predicted from        the eating behavior such as reduced feed consumption (combined        with a good health status to be sure the animal is not ill),    -   behavior such as eating behavior, e.g. how long time the animal        is at a feeding trough (in loose-housing systems the feeding        trough may be a virtual feeding trough as the animal can select        different places for eating), how long time the animal is        actually eating, how often the animal is eating, how much the        animal eats when eating and how much the animal eats per day,    -   indications of illness, such as reductions and/or changes in the        feed consumption and/or eating behavior.

Another aspect of the invention relates to a system for determining theidentity of an individual animal from the appearance and/or topology ofthe back of the animal, the system comprises

-   -   An reference imaging unit for providing reference images of at        least one identified animal, where the reference imaging unit        comprises        -   at least one identity determining device for determining the            identity of the identified animal,        -   at least one camera for obtaining at least one image of the            back of the identified animal,        -   at least one database for storing at least information of            identity of at least one identified animal and at least one            image of the back of the identified animal, and        -   data transmission means for transmitting data from the            identity determining device and the camera to the database,    -   An imaging unit for obtaining at least one image of the back of        at least one un-identified animal, where the imaging unit is        connected to the database for transmission of data from the        imaging unit to the database, and    -   At least one processing means connected to the database for        comparing the at least one image from an un-identified animal        with at least one reference image and based on this comparing        determine whether the un-identified animal corresponds to the        identified animal.

The image obtained by the system may be a 3D image and also thereference image may be a 3D image and thus a reference 3D image.

The imaging unit of the system may comprise at least two cameras. Thesetwo cameras may be located at any distances from each other making itpossible to cover areas of interest. Preferably the at least two camerasare located at mutual distances of within 15 M, such as within 10 M,e.g. within 5 M from each other for simultaneously obtaining at leastone image by each camera of the back of at least one un-identifiedanimal, where the at least two cameras are connection to the databasefor transmission of data from the cameras to the database and where thedatabase constructs at least one 3D image of the animal based on theimages from the at least two cameras.

The at least one camera of the reference imaging unit and of the imagingunit may each be one or more cameras selected from the group of rangecameras, stereo cameras, time-of-flight cameras. Preferably thereference imaging unit and the imaging unit comprises cameras of equaltype.

The reference imaging unit and/or the imaging unit may comprise at leastone range camera with a depth sensor and a 2D camera, such as a RGBcamera. The reference imaging unit and/or the imaging unit may alsocomprise at least a time-of-flight camera.

Preferably the reference imaging unit and the imaging unit of the systemare configured for acquiring topographic images.

The system may be set up such that the camera of the reference imagingunit is activated to obtain an image of the animals back when an animalis close to the identity determining device and the identity of theanimal has been registered. A triggering mechanism as describedelsewhere may be a part of the system.

The system may also comprise ID tags. Such ID tags may be connected toanimals to be identified. ID tags may be visual and/or electronic IDtags. Electronic ID tags may be electronic ear tags and/or electronic IDtags attached to an animal such as in a collar. A single animal may bemarked with one or more ID tags such as at least one visual ID tagand/or at least one electronic ID tag. An example is at least one visualear ID tag combined with at least one electronic ID tag in a collar.Another example is at least one visual ear ID tag combined with at leastone electronic ear ID.

The system may also comprise identity determining device such as acamera suitable to obtain images of visual ID tags. The identitydetermining device may also comprise an ID reader capable of registeringan animal identity based on electronic identity markers located in or atan animal.

The system comprises a database which may store multiple referenceimages of a single animal. The database may store multiple referenceimages of a single animal from each day. Such reference images may beobtained with different time interval during a period of a day, twodays, three days, four days, five days, six days, a week or at longerintervals. The time between obtaining reference images of an animal maybe determined such that each time the animal is in an area of anidentity determining device the system determines the identity of theanimal and obtain at least one reference image of the back of theanimal. The system may store reference images and/or other images of ananimal e.g. for the animal's entire lifetime or for the time the animalis kept at the location, e.g. at the farm where the images are obtained.Images may also be stored for much longer time and may be used asstatistical data for different purposes, such as evaluation of feedtypes, feeding methods and breeding, e.g. value of specific crossings orvalues of specific male animals.

The system as described herein may also be used for monitoringindividual animals, such as in relation to health status and risk ofillness. Such monitoring may be based on any changes of the bodyobserved, e.g. from day to day or by comparing data obtained from anumber of days, such as two days, three days or more. The system mayautomatically monitor each animal in a population and certain thresholdvalues based on changes in the registered information may be included inthe system, such that an alarm or information note is created by thesystem when an animal's body change too much within a specified timeperiod.

Preferably the database stores at least reference images of a singleanimal for at least one month, such as at least two months, such as atleast half a year, e.g. at least one year. Preferably the databasestores at least reference images of a single animal until this animal isno longer within the animal population or no longer present e.g. due tobeing sold or dead.

The system comprises processing means which may select features from theat least one image and the at least one reference images beforecomparing these features. Examples of types of features are describedelsewhere herein. The processing means of the system may comparefeatures from at least one image with features from at least onereference image by any known comparing method.

For comparing features the processing means may use a method wherepredefined feature vectors of an animal for preselected distancescalculated from the ground or floor are compared. When comparing atleast one feature from at least one image with at least onecorresponding feature from at least one reference image the processingmeans may determine and compare areas of layers of 3D-images. Such areasmay be part of feature vectors or may constitute features for e.g.sequential comparing at least one image with at least one referenceimage.

When establishing features from images i.e. from at least one image ofan un-identified animal, and these at least one image in fact are two ormore images, these images may be obtained within a short period of timesuch as within less than 20 seconds, e.g. within less than 10 seconds,such as within less than 5 seconds, e.g. within less than 3 seconds,such as within less than 2 seconds. For such series of images a featuremay be established based on a single image or may be an average based ontwo or more images of the series.

When establishing features from reference images i.e. from at least onereference image of an identified animal, these features may beestablished from one or more images from series of an identified animaland in a manner as described for images of un-identified animals.

Areas of layers of an animal may be determined for layers with apre-selected plane distance. Such a pre-selected plane distance may beabout 8 cm, such as about 7 cm, e.g. about 5 cm, such as about 4 cm,e.g. about 3 cm relative to a predefined fix point. Preferably apre-selected plane distance is about 5 cm. Hereby the processing meanscan make a calculation of the area of an animal such as the area of theback at horizontal planes with mutual distances of the pre-selectedplane distance e.g. 5 cm. Such areas of layers may constitute featuresfor comparing at least one image with at least one reference image.

Areas of layers may also be used to determine percentage of an animalabove a preselected level. Different areas of the animal back determinedat pre-selected plane distances and calculated as percentages relativeto a preselected level may constitute features for comparing at leastone image with at least one reference image. An example: A pre-selectedlevel may be 135 cm above ground level and at this level the area of ahorizontal plane of the animal back is calculated. A pre-selected planedistance may be 5 cm and the area at these levels i.e. at 140 cm, 145cm, 150 cm, 155 cm etc above ground level can be determined. The areascan be converted into percentages in respect of the area at thepre-selected level i.e. in this example at 135 cm, and these percentagesmay constitute features for comparing at least one image with at leastone reference image.

Determining features to be used when comparing at least one image withat least one reference image may be based on plane areas as describedabove and may be performed for pre-selected distances calculated fromthe ground or floor. Such pre-selected distances can be selected due tothe height of the animal species, animal race and/or animal type whichshould be identified. A pre-selected distance for animals with atmaximum height of e.g. 180 cm may be 140 to 180 cm and can be combinedwith a pre-selected plane distance of e.g. 5 cm such that areas ofanimals or the back of animals are determined for distances of 140 cm,145 cm, 150 cm, 155 cm, 160 cm, 165 cm, 170 cm, 175 cm and 180 cm aboveground level. Such areas may be used as exact numbers and/or aspercentage of the area at a pre-selected level e.g. 140 cm above groundlevel and may hereby be used as features for comparing at least oneimage with at least one reference image.

Instead of determining the areas at different planes the planes can beassumed to be a ground level for determining the volume of the animalback above this ground level i.e. volume of the animal above differentheights of the animal. Each plane e.g. 120 cm, 125 cm, 130 cm etc. aboveground level may thus have its own ground level and for each of theseground levels the volume above this ground level can be determined. Oneor more of these volumes can be used as a feature for comparing at leastone image with at least one reference image. The planes for determiningvolumes of animal backs above the planes may be selected due to themaximum or average height and/or size of the animal species, race, typeetc. to be identified.

Reference images may be acquired at a location where the cows are wellpositioned relative to a 3D camera under which each cow in the flockpasses one or more times per day. At this location each cow's RFID tagis read such that cow ID and 3D images can be paired. Over time a largelibrary with images of all cows is built up. This library of images canbe used for identifying cows from images of the cows' back acquired atother locations at the farm. The library can also be used to follow thehealth status of each cow over time.

When determining the identity of an animal by comparing at least onefeature from at least one image with at least one corresponding featurefrom at least one reference image the process of determining theidentity of an animal may be performed sequential e.g. by firstcomparing coarse or overall features obtained from the image andreference images and hereby sorting out the reference images which donot meet the overall features. Second comparison may be performed basedother less overall and/or more specific features obtained from the imageand reference images. A third, fourth etc comparison of at least onefeature obtained from at least one image may be compared with at leastone corresponding feature obtained from at least one reference imageuntil a match is obtained between the at least one image and the atleast one reference image where the at least one reference image areimages of a single animal.

An example of performing a sequential determination of an animal basedon the invention as described herein may comprise comparing featuresdetermined in at least one image with the corresponding featuresdetermined in at least one reference image:

-   -   1^(st) comparing: Height of the animal (Q),    -   2^(nd) comparing: Color pattern of the skin (U),    -   3^(rd) comparing: Length of the back (V),    -   4^(th) comparing: Contour line along the backbone (W),    -   5^(th) comparing: distance between two pre-selected points e.g.        distance between the back hips (X),    -   6^(th) comparing: location and/or sizes and/or depth of cavities        (Y),    -   7^(th) comparing: contour plots or planne areas for different        planes of the animal (Z),    -   8^(th) comparing volumes above selected planes of the animal.

The example described with sequential determination of the identity ofan animal may include any suitable feature and be performed in anysuitable order until all tested features obtained from at least oneimage of an un-identified animal corresponds to all the correspondingfeatures obtained from at least one reference image of an identifiedanimal, and where the at least one reference image of an identifiedanimal if being more than one reference image all reference images arefrom the same individual. Determining the identity of an animal may alsobe performed by comparing feature vectors. In the example aboveindicating 7 comparisons in a sequential determination, the features areindicated by a letter, each of these letters may correspond to a featuregroup each comprising different possibilities e.g. for height of animalQ₁ is different from Q₂. A feature vector may thus comprise at least onefeature from each feature group and such feature vectors may be comparedto determine the identity of an animal.

As an example of comparing feature vectors and un-identified animal mayhave a feature vector of [Q, U, V, W, X, Y, Z] and assuming that onlytwo possibilities exist within each feature group a comparison offeature vectors may be performed as indicated below, where only alimited number of the possible feature combinations are shown in featurevectors:

-   -   Feature vector obtained for un-identified animal: [Q₁, U₂, V₁,        W₂, X₁, Y₂, Z₁]    -   Feature vector obtained for identified animal No. 1: [Q₁, U₁,        V₁, W₂, X₁, Y₂, Z₁]    -   Feature vector obtained for identified animal No. 2: [Q₁, U₁,        V₂, W₁, X₂, Y₁, Z₂]    -   Feature vector obtained for identified animal No. 3: [Q₁, U₁,        V₁, W₂, X₁, Y₂, Z₂]    -   Feature vector obtained for identified animal No. 4: [Q₁, U₂,        V₂, W₁, X₂, Y₁, Z₂]    -   Feature vector obtained for identified animal No. 5: [Q₁, U₂,        V₁, W₂, X₁, Y₂, Z₁]    -   Feature vector obtained for identified animal No. 6: [Q₂, U₁,        V₂, W₁, X₂, Y₁, Z₁]    -   Feature vector obtained for identified animal No. 7: [Q₂, U₁,        V₁, W₂, X₁, Y₂, Z₁]    -   Feature vector obtained for identified animal No. 8: [Q₂, U₁,        V₂, W₁, X₂, Y₁, Z₂]    -   Feature vector obtained for identified animal No. 9: [Q₂, U₂,        V₁, W₂, X₁, Y₂, Z₁]    -   Feature vector obtained for identified animal No. 10: [Q₂, U₂,        V₂, W₁, X₂, Y₁, Z₂]

By comparing the feature vectors the only match between the featurevector for the un-identified animal corresponds to the feature vectorfor animal No. 5, it can then be concluded that the un-identified animalis animal No. 5. Performing a sequential comparison with the featuresmentioned in the feature vectors, the 1^(st) comparison based on featureQ will match to animal No. 1, 2, 3, 4 and 5, which are used for the nextcomparison. 2^(nd) comparison based on feature U will match to animalNo. 4 and 5, and of these the 3^(rd) comparison based on feature V willmatch with only animal No. 5. When an un-identified animal is identifiedas described herein, the system of the invention may by itself be usedfor obtaining different kind of information for identified animals, thesystem may also be extended to provide further information which can bestored together with the identity of an identified animal identifiedaccording to the method as described herein.

The comparison may also be performed by using a neural networkimplemented as a deep learning system. Both Neural Networks and deeplearning processes are known by experts in the art of image processing.For example: A cow and its orientation in the image can be found usingtemplate matching techniques, which are also known in the art. Once anunknown cow appears in the image, features such as height, colorpatterns, length of back, height contour of back bone, distances betweenpreselected points, cavities, areas at various heights and volumes abovethese areas may be calculated. A supervised or unsupervised neuralnetwork that has been trained on a large number of reference images fromeach cow in the flock can then be applied. The trained neural networkcan then identify the unknown cow by comparing the unknown cow with thelibrary images of all cows.

The system may comprise means for determining feed consumption of atleast one of said animal. Such means may comprise

-   -   a feeding area imaging unit for providing images of a feeding        area, and    -   processing means configured for assessing the amount of feed        consumed by each identified animal by determining the reduction        of feed in subsequent images of the feeding area in front of        each identified animal.

Processes of determining feed intake or reduction of feed in a feedingarea based on comparing the amount of feed in subsequent images of thefeeding area are described in WO2014/166498 (System for determining feedconsumption of at least one animal', Viking Genetics FMBA).

The feeding area imaging unit may be the imaging unit for obtaining atleast one image of the back of at least one un-identified animal suchthat the imaging unit obtains images of the back of at least oneun-identified animal as well as of a feeding area and where at least oneun-identified animal is capable of eating feed from the feeding area.Preferable the at least one image covers the back of at least oneun-identified animal together with a feeding area in front of thisun-identified animal.

The system may determine feed consumption from at least two images ofthe same feeding area and where the feed reduction is calculated as thedifference of feed volume within a feeding area established from the atleast two images.

The imaging unit of the system may be configured for continuouslyimaging at least a part of a feeding area. It is also possible to havean imaging unit which is configured for imaging an area including afeeding area at predefined and/or selected time points. The at least onecamera of the system may be pivotable around at least one axis making itpossible to adjust the at least one camera in different directions toobtain at least one image of at least one animal or of at least oneanimal and the feeding area in front of the at least one animal.

The system may also comprise at least one camera rail and/or camera wirefor positioning the at least one camera relative to at least one animaland/or a feeding area in front of the at least one animal. Rails and/orwires may be suspended or stretched above an area where the animals tobe identified stay and this may be an indoor area and/or an outdoorarea.

The system may also comprise at least one drone, the drone beingconnected to at least one camera and said drone being capable of flyingabove at least one animal to let the at least one camera obtain at leastone picture of the at least one animal. The at least one camera on thedrone may be fixed or pivotable. A pivotable camera may be turned due toinput from camera position means obtaining information regardinglocation of animals. Information of location of animals may be based onsignals from at least one electronic ID tag at an animal and/or may bebased on signals obtained from an infrared camera capable of detectinglive animals.

A drone may be used inside a shed or stable shielding animals and/or maybe used outside at areas where animals to be identified can be locatedsuch as in the field and/or in an enclosure. A drone may be used forobtaining images of un-identified animals and at other times it may beused for obtaining reference images of animals by also obtaininginformation from the animal from at least one electronic ID tag.

A drone when used outside together with the invention described hereinmay be used for different purposes such as identification of e.g. dairycows in grassing systems, for determining the health status of ananimal, etc.

DETAILED DESCRIPTION OF THE FIGURES

FIG. 1 illustrates eating cows in a cowshed (1) in which a system of thepresent invention is installed. Cameras (4) mounted above the cows (3)obtain images of the back of the cows and forward these images to adatabase and processing unit (6). The cows are marked by ID-tags such asear-tags (5), however, if the cows are walking freely in the stable itmay not be possible to identify the cows from the ID-tags. The systemmay be configured to obtain images of the back of the cows as well asthe feed (2) in front of the cows. From the obtained images it ispossible to identify each cow and estimate the amount of feed intake foreach of these cows.

FIG. 2 illustrates examples of different pre-selected points at the backof a cow. Such preselected points can be used to extract furtherinformation from the images, such as lengths between different points,angles of lines between different points etc.

FIG. 3 illustrates examples of data or features established in respectof the back of an animal, here the back of a cow. The data or featuresindicated are:

-   -   total area of the cow's back which is located higher than 70% of        the cow's maximum height (large ellipse indicated by a stippled        line),    -   total area of the cow's back which is located higher than 90% of        the cow's maximum height (two small ellipses within the large        ellipse),    -   length of a profile along the spine at a height higher than 70%        of the cow's maximum height (illustrated by a dotted line in the        longitudinal direction of the cow from the neck to the tail        head),    -   distance between hip bones at their maximum height (illustrated        by a thick vertical line crossing through the small ellipse at        the rear of the cow's back),    -   width of the body that is higher than 70% of the cow's maximum        height at e.g. 7 locations along the length of the cows body        (illustrated by thin vertical lines within the large ellipse),    -   colour pattern, if any (not illustrated).

FIGS. 4A-B illustrate the height profile along the backbone of two cowsfrom the tail head (left part of graph) to the neck (right part of thegraph) of a cow which is slightly higher than 1.6 M (FIG. 4A) and a cowwhich is about 1.7 M (FIG. 4B).

FIG. 5 illustrates a Mesa Imaging 3D reconstruction of the part of a cowwith a height above 90 cM from floor level.

FIG. 6 illustrates the back of a cow.

FIG. 7 illustrates the back of the cow of FIG. 6 with indications ofsome data/features which can be used in the analysis. The steps 1-6 arefurther explained in Example 2 and represent:

-   -   1: Length of backbone and a height profile along the backbone of        the cow i.e. a longitudinal height profile.    -   2: Contour line of the cow at a predetermined height of 90 cm        from the floor.    -   3: Contour plane for the pixels located higher than a cow height        corresponding to the 80% quantile height subtracted 8 cM.    -   4: Contour plane for the pixels located higher than a cow height        corresponding to the 80% quantile height subtracted 2 cM.    -   5: An arbitrary triangle made based on the location of the left        and right hip bones and the tail head, where e.g. the angle at        the tail head can be determined.    -   6: The maximum width in the transversal direction of the cow at        the location where the cow is widest and along this line a        height profile can be determined i.e. a transversal height        profile

FIG. 8 illustrates area determination based on rescaled data obtainedfrom the part of a cow with a height above 90 cM from the floor. Theareas beneath the graphs (and e.g. above 90 cM-line) can be determined.

FIGS. 9A-D and 10A-D illustrate different thickness profiles and heightprofiles at predetermined heights of two cows. In each figure the datais rescaled to 100 pixels (=X-axis) and thickness measured in pixels(=Y-axis) or height above floor measured in cM (=Y-axis). The left endof the graph corresponds to the neck region and the right end of thegraph corresponds to the tail region.

-   -   FIGS. 9A and 10A: Thickness profile for a cow measured 90 cM        above floor level. Each axis indicates pixels.    -   FIGS. 9B and 10B: Thickness profile for a cow measured along the        line indicated by step 3 in FIG. 7 i.e. at a cow height        corresponding to the 80% quantile height subtracted 8 cM.    -   FIGS. 9C and 10C: Thickness profile for a cow measured along the        line indicated by step 4 in FIG. 7 i.e. at a cow height        corresponding to the 80% quantile height subtracted 2 cM.    -   FIGS. 9D and 10D: Longitudinal height profile along the backbone        of a cow. X-axis indicates pixels, Y-axis indicates cM from the        floor.

FIG. 11 illustrates a transversal height profile of a cow at theposition where the cow was thickest (measured above 90 cm from thefloor). The data is rescaled to 40 pixels. X-axis indicates pixels,Y-axis indicates cm from the floor.

FIG. 12 illustrates determination of a cow based on neural network, suchas a deep learning system. A number of features from a cow to beidentified are entered into the system and an output is obtained withestimated and ranked likelihood of different matches.

Example 1

The method was developed by testing whether a number of Jersey andHolstein cows could be determined/identified from each other based onimages of their backs. At a Danish farm with dairy cows 3-D images ofcow's backs were provided. The system for obtaining images included a 3Dcamera (Swiss Ranger 4500 from Mesa Imaging, Switzerland, which is an IP67 camera suitable for rooms with dust and moisture). In parallel withthe 3D camera, two Basler black-and-white industrial cameras weremounted. The cameras were mounted 4.5 meter above the floor level. Thedistance from the camera to the upper part of the back of the cows wasabout 2.7-3 meter depending on the height of the cows. Images of theback of the cows were obtained when the cows were on their way to themilking station and at a position where the cows walked one afteranother. Hereby images were obtained with only one cow at each image.From the obtained 3D-images contour plots were performed as furtherdescribed in Example 2 although at 148 cM, 153 cM, 158 cM, 165 cM and172 cM above the floor level. The area of the cow's back within each ofthe contour plots at the indicated heights were determined. Based on thearea within the mentioned contour plots the 16 cows were easilyidentified without mixing-up the identities. In this experiment to testwhether the cows actually could be identified from the images, the cowswere also identified by different visible marks painted on the back ofeach cow. These marks were only used to verify that the identificationbased on the other features was correct.

FIGS. 4A-B illustrate further features which can be used whenidentifying animals. The figure illustrates a contour line along thebackbone. The position of the backbone is illustrated in FIG. 7.

FIG. 4A: Height profile in the longitudinal direction of the cow alongthe backbone of a low cow.

FIG. 4B: Height profile in the longitudinal direction of the cow alongthe backbone of a heigher.

Both the length of the backbone as well as the height profile along thebackbones can be used as features when identifying animals such as cowsas explained in Example 2. In the experiment about 6 images of each cowwere obtained with about 1 second between each exposure. Analysis ofeach image as outlined above and comparison of data obtained from theimages for each cow and between cows clearly showed much less variationfor the images of one cow than between different cows.

Example 2

The identification method was further tested in another experiment withdairy cows of the Jersey race. 3-D images of cow's backs were providedwith a system included a 3D TOF (time-of-flight) camera (Swiss Ranger4500 from Mesa Imaging, Switzerland).

Also two Basler black-and-white industrial cameras were used. The threecameras were connected to a computer making it possible to store andanalyze images. The 3D camera was located 3.2 M above the floor at theentrance to the milking station and where the corridor has a width ofabout 1 M. In a wall along the corridor an ID-reader was located toobtain a signal from the ear tag each time a cow passed the ID-reader. Atrigger signal was sent to the computer each time a cow passed theID-reader. The trigger signal prompted the computer to store one imagefrom each of the three cameras with 0.5 sec between the exposures. TheID-reader also stored the ID of the cow obtained from the ear tag, andthese ID's were only used to verify the developed identification methodbased solely on the images of the cow's back. The two black-and-whitecameras were only used to obtain images to see the cows and theenvironment to check if something seemed to be strange. The images fromthe black-and white cameras were not used for the identificationprocess.

FIG. 5 is a Mesa Imaging 3D reconstruction of the part of a cow with aheight above 90 cM from floor level. The same data of the cow is shownin a 3D contour plot of height in FIG. 6. For each 3D image obtained theimages were analyzed in different steps to obtain data and PCA-scores tocalculate a vector for each cow. FIG. 7 indicates from where at thecow's back the data was obtained. The steps in the analysis aredescribed below and indicated in FIG. 7:

-   -   a) Step 1: Calculating a height profile in the longitudinal        direction of the cow along the backbone. A curve was calculated        to describe the height profile along the backbone from the ‘tail        head’ to the ‘neck point’ where these end positions in this        measurement were determined by the point where the body        thickness was less than 38% of the widest width of the cow.    -   b) Step 2 a (Indicated as Step 2 in FIG. 7): Determining a        contour line of the cow at a predetermined height of 90 cm from        the floor. The contour line of the cow was determined for the        same length as for the height profile in step 1 i.e. from the        ‘neck point’ to the ‘tail head’. The area within this contour        line was determined as the area beneath the graph ‘height’ and        above 90 cM in FIG. 8 as described further in Step 2 b.    -   c) Step 2 b—Further analysis of data from Step 2 a: Distribution        of heights in the image pixels located within the 90 cm contour        line. Different distributions are illustrated as graphs in FIG.        8 where all of the pixels within the 90 cm contour line are        sorted according to their corresponding height of the cow and        this is shown as a function of the percent of pixels        corresponding to the cow height between 90 cm and a        predetermined height above 90 cm or the total height of the cow.        In FIG. 8 this distribution or area determination is shown for a        cow of a maximum height of 130 cM indicated by the graph        ‘height’, where the graph illustrates the percentage of pixels        below a certain height of the cow but above 90 cM from the        floor. It can be seen that about 40 percent of the pixels (in        the range above 90 cm) are located beneath 120 cm.    -   d) Step 2 c—Further analysis of data from Step 2 b: From the        distribution of heights as determined in step 2 b a 80% quantile        height was determined as 80% of the maximum cow height. This        graph is shown as ‘80%’. The maximum cow height was determined        as an average of the value of the 50 pixels indicating the        tallest locations of the cow. In the example with the data in        FIG. 8 the maximum height is 130 cM and the 80% quantile is 104        cM. The area below the graph ‘80%’ and above 90 cM was        determined.    -   e) Step 3: Determination of a delimitation of a contour plane        for the pixels located higher than a cow height corresponding to        the 80% quantile height subtracted 8 cM. The area within this        contour line was determined. In the example with the data in        FIG. 8 the contour plane is determined at a cow height of 104        cM−8 cM=96 cM. The area is determined as the area below the        graph ‘80%−8 cM’ and above 90 cM.    -   f) Step 4: Determination of a delimitation of a contour plane        for the pixels located higher than a cow height corresponding to        the 80% quantile height subtracted 2 cM. The area within this        contour line was determined. In the example with the date in        FIG. 8 the contour plane is determined at a cow height of 104        cM−2 cM=102 cM. The area is determined as the area below the        graph ‘80%−2 cM’ and above 90 cM.    -   g) Step 5: Determination of the points in the images        corresponding to the location of the outer part of the hip bones        which was defined as the location in the image where the contour        plane determined in step 3 is widest. An virtual ? arbitrary        triangle was made based on the location of the left and right        hip bones and the tail head as determined in step 1 and in this        triangle the angle at the tail head was determined as well as        the distance between the left and right hip bones.    -   h) Step 6: Determining the maximum width in the transversal        direction of the cow and at the location where the cow is widest        and calculating a height profile along the maximum width i.e. a        transversal height profile.

Analysis of the Data

The data obtained as described in the eight items above was converted todata making it possible to perform statistical analysis.

The contour planes determined in steps 2 a (90 cM height), 2 c (80%quantile height) and 4 (80% quantile height minus 2 cM) were transformedinto thickness profiles. Such thickness profiles have different lengthsbetween cows as the length of the cows differs and therefore thethickness profile of each cow was rescaled to a fixed length of 100pixels. In a similar way the longitudinal height profile of step 1 wasrescaled to a fixed length of 100 pixels. The transversal height profileof step 6 was rescaled to a fixed length of 40 pixels. Rescaling wasperformed as a simple proportion calculation based on the actual cowlength or width and a length of 100 (or 40 if 40 pixels are therescaling dimension) hereby a value Z_(n) for a cow of a length 80 cM isrescaled by (Z_(n)/80)×100=1.25 Z_(n) or if Z_(m) is for a cow of alength 115 cM the Z_(m)-value is rescaled to (Z_(m)/115)×100=0.87 Z_(m).

The entire data set for each image at this stage comprised 449variables:

-   -   1. The area determined within the 90 cM contour line as        described in step 2 a (1 variable)    -   2. The area determined within the contour line delimited by the        80% quantile height as described in step 3 (1 variable)    -   3. The area determined within the contour line delimited by the        80% quantile height minus 2 cM as described in step 4 (1        variable)    -   4. The 80% quantile height (1 variable)    -   5. The angle between the lines from the tail head to the right        and left hip bone as described in step 5 (1 variable)    -   6. The maximum width as described in step 6 (1 variable)    -   7. The length of the contour line determined at the cow's height        of 90 cm as described in step 2 a (and step 1) (1 variable)    -   8. The length of the contour line delimited by the 80% quantile        height as described in step 3 (1 variable)    -   9. The length of the contour line delimited by the 80% quantile        height minus 2 cM as described in step 4 (1 variable)    -   10. The thickness profiles at the cow's height of 90 cm rescaled        to 100 pixels (100 variables) and illustrated in FIGS. 9A and        10A.    -   11. The thickness profiles at the cow's height determined at the        80% quantile height as described in step 3 and rescaled to 100        pixels (100 variables) and illustrated in FIGS. 9B and 10B.    -   12. The thickness profiles at the cow's height determined at the        80% quantile height minus 2 cM as described in step 4 and        rescaled to 100 pixels (100 variables) and illustrated in FIGS.        9C and 10C.    -   13. The height profile in the longitudinal direction as        described in step 1 and rescaled to 100 pixels (100 variables)        and illustrated in FIGS. 9D and 10D.    -   14. The height profile along the maximum width as described in        step 6 and rescaled to 40 pixels (40 variables) and illustrated        in FIG. 11.

To further compress the data a 6 PCA model (PCA=principal componentanalysis) was developed with up to 15 principal components (PC scores)for each data set (feature set) with the following combination of dataand where the variable number refers to the list above:

-   -   a) Variable 1 to 9 (9 PC scores)    -   b) Variable 7+10 (15 PC scores)    -   c) Variable 8+11 (15 PC scores)    -   d) Variable 9+12 (15 PC scores)    -   e) Variable 10+13 (15 PC scores)    -   f) Variable 11+14 (15 PC scores)

The person skilled in the art knows how to perform a principal componentanalysis, and this will not be further described.

The original lengths of the curves were included in the calculation ofthe PC scores hereby the knowledge of the length of the individual cowwas maintained. With the PC scores a total of 449 variables were reducedto 85 variables.

Identification of the Individual Cow

The sequence of numbers i.e. the PC scores for a cow to be identifiedwas compared to the average feature PC of each of the cows in the herd.A cow was identified when the average feature PC for this cow resembledan average feature PC calculated for one cow more than it resembledaverage feature PCs calculated for the other cows in the herd. Inpractice the calculation was performed by creating the dot productbetween each average vector X_(k) for each cow ‘k’ in the herd and thevector X_(u) for the un-identified cow i.e. the cow to be identified:

${\cos\left( v_{k} \right)} = \frac{\overset{->}{X_{k}} \cdot \overset{->}{X_{u}}}{{\overset{->}{X_{k}}}{\overset{->}{X_{u}}}}$

where v_(k) is the angle between the two vectors {right arrow over(X_(k))} and {right arrow over (X_(u))}, and |{right arrow over(X_(k))}| and |{right arrow over (X_(u))}| are the length of each of thevectors. If the vector for an un-identified cow resemble a vector for acow in the herd then cos(v_(k)) will be close to +1 (plus 1), whereas ifthese two cows are very different cos(v_(k)) will be close to −1 (minus1).

The shown model for analysis is very simple and over fitting is nearlyunlikely. The model can be extended and improved ongoing as morepictures are obtained for each cow. It is also simple to identifydeficient images and avoid the use of these for identification of a cowor when extending the calculation of an average vector for each of thecows.

The method as described above was tested with 9 principal components forfeatures indicated under item a) in the list above and either 15, 14,13, 12, 11, 10, 9, 8, 7 or 6 principal components for each of theremaining features indicated under item b) to f) in the list above. Thebest result was obtained by using 9 scores for features of item a) and 7scores for each of the features of item b) to f).

The analysis as described in example 2 was performed for about 5 imagesfor each cow representing in total 27 cows, in total 137 images. Theimages representing one cow were obtained at different times of the dayand at different days. Of the 137 images 116 were immediately correctlyconnected to the right cow when using 9 scores for features of item a)and 7 scores for each of the features indicated under item b) to f) inthe list above. When making an average of the 5-6 images obtained foreach cow although obtained at different days the identification of allof the cows were correct. Extending the analysis to be based on morefeatures obtained from the images and/or from features obtained frommore than one image of a cow where the images are obtained e.g. with avery short time span e.g. of 0.1-1 e.g. 0.5 seconds would make sure thecorrect identification is performed.

Further Details

1. A method for determining the identity of an individual animal fromthe natural appearance and/or topology of the back of said animal, saidmethod comprising

-   -   Obtaining at least one image of the back of an un-identified        animal,    -   Extracting data from said at least one obtained image, said        extracted data relating to the natural appearance and/or        topology of the back of the animal,    -   Comparing said extracted data extracted from at least one image        of an un-identified animal with reference data extracted from at        least one reference image of a back of an identified animal        where information of the identity of the identified animal is        connected to said at least one reference image, and    -   Determining based on said comparing whether said un-identified        animal corresponds to said identified animal.

2. The method according to item 1 wherein at least one reference imageof the back of an identified animal is obtained at least once a month,preferable the reference image is obtained at least every second day,more preferable the reference image is obtained at least once a dayand/or said animal is selected from the group of cattle, cows, dairycows, bulls, calves, pigs, sows, boars, castrated males, piglets,horses, sheep, goats, deer.

3. The method according to any of the items 1 to 2 wherein said at leastone reference image of the back of an identified animal is obtained by

-   -   providing the identification number of an animal, hereby the        animal being an identified animal,    -   providing at least one image of the back of said identified        animal, and    -   storing in a database said identification number of the        identified animal together with said at least one image of the        back of said identified animal said image hereby being a        reference image.

4. The method according to any of the items 1 to 3 wherein said imageand said reference image are topographic images of the back of theanimals, such as 3D images e.g. multiple layers of 3D-images.

5. The method according to any of the items 1 to 4 wherein saidcomparing of extracted data extracted from said image with extracteddata extracted from said reference image is performed by comparing atleast one feature and/or at least one feature vector obtained from thesaid image with a corresponding feature and/or feature vector obtainedfrom said reference images, such features and/or feature vector maycomprise or be based on values of the area of multiple layers of said3D-image and/or values selected from the group of topographic profile ofthe animal, such as the height of the animal, the broadness of theanimal, contour line along the backbone of the animal, the length of theback, contour plots for different heights of the animal, volume of theanimal above different heights of the animal, size of cavities, depth ofcavities, the distance between two pre-selected points at the animal,where said pre-selected points may be selected from the group of righthip, left hip, right shoulder, left shoulder, tail head, neck, (1) leftforerib, (2) left short rib start, (3) left hook start, (4) left hookanterior midpoint; (5) left hook, (6) left hook posterior midpoint, (7)left hook end, (8) left thurl, (9) left pin, (10) left tail head nadir,(11) left tail head junction, (12) tail, (13) right tail head junction,(14) right tail head nadir, (15) right pin, (16) right thurl, (17) righthook end, (18) right hook posterior midpoint, (19) right hook, (20)right hook anterior midpoint, (21) right hook start, (22) right shortrib start, and (23).

6. A system for determining the identity of an individual animal fromthe natural appearance and/or topology of the back of said animal, saidsystem comprising

-   -   at least one camera for obtaining at least one image of the back        of an un-identified animal,    -   at least one database or admission to at least one database for        storing data related to at least one reference image of the back        of an identified animal and for storing data related to at least        one image of the back of an un-identified animal,    -   data transmission means for transmitting data from said at least        one camera to said database,    -   at least one processing means connected to said database, said        processing means being configured for comparing extracted data        from said at least one image from an un-identified animal with        extracted data from at least one reference image where said        extracted data is related to the natural appearance and/or        topology of the back of the animal and based on this comparing        determine whether said un-identified animal corresponds to said        identified animal.

7. A system for determining the identity of an individual animal fromthe natural appearance and/or topology of the back of said animal, saidsystem comprising

-   -   A reference imaging unit for providing reference images of at        least one identified animal, said reference imaging unit        comprising        -   i. at least one identity determining device for determining            the identity of said identified animal,        -   ii. at least one camera for obtaining at least one image of            the back of said identified animal,        -   iii. at least one database or admission to at least one            database for storing at least information of identity of at            least one identified animal and at least one image of the            back of said identified animal,        -   iv. data transmission means for transmitting data from said            identity determining device and said camera to said            database,    -   An imaging unit configured for obtaining at least one image of        the back of at least one un-identified animal, where said        imaging unit is connected to said database for transmission of        data from said imaging unit to said database,    -   At least one processing means connected to said database, said        processing means being configured for comparing extracted data        from said at least one image from an un-identified animal with        extracted data from at least one reference image where said        extracted data is related to the natural appearance and/or        topology of the back of the animal and based on this comparing        determine whether said un-identified animal corresponds to said        identified animal.

8. The system according to item 7, wherein said image is a 3D image andsaid reference image is a reference 3D image and/or said at least onecamera of said reference imaging unit and said imaging unit each is oneor more cameras selected from the group of range cameras, stereocameras, time-of-flight cameras such as a range camera comprising adepth sensor and a 2D camera, such as a RGB camera and/or.

9. The system according to any of the items 7 to 8 wherein said cameraof said reference imaging unit is activated to obtain an image of theanimals back when an animal is close to said identity determining deviceand the identity of the animal has been registered.

10. The system according to any of the items 7 to 9 wherein saiddatabase stores multiple reference images of a single animal such asmultiple reference images of a single animal from each day.

11. The system according to any of the items 7 to 10 wherein saidprocessing means determine feature vectors of an animal for preselecteddistances calculated from the distance from the ground or floor and/orsaid feature vectors are areas of layers of 3D-images and/or saidpreselected distances are between 70 and 180 cm.

12. The system according to any of the items 7 to 11 further comprisingmeans for determining feed consumption of at least one of said animalsuch as

-   -   a feeding area imaging unit for providing images of a feeding        area,    -   processing means configured for assessing the amount of feed        consumed by each identified animal by determining the reduction        of feed in subsequent images of the feeding area in front of        each identified animal.

13. The system according to item 12 wherein said feeding area imagingunit is said imaging unit for obtaining at least one image of the backof at least one un-identified animal such that said imaging unit obtainsimages of the back of at least one un-identified animal as well as of afeeding area.

14. The system according to any of the items 12 to 13 wherein feedconsumption is determined from at least two images of the same feedingarea and feed reduction is calculated as the difference of feed volumebetween the at least two images.

15. The system according to any of the items 12 to 14 wherein theimaging unit is configured for continuously imaging at least a part of afeeding area.

1. A method for determining the identity of an individual livestockanimal in a population of livestock animals with known identity, themethod comprising the steps of: acquiring at least one 3D image of theback of a preselected livestock animal; extracting data from said atleast one 3D image, said extracted data relating to the anatomy of theback and/or topology of the back of the preselected livestock animal;and comparing and/or matching said extracted data against reference datacorresponding to the anatomy of the back and/or topology of the back ofthe livestock animals with known identity, thereby identifying thepreselected animal, wherein said reference data are extracted from atleast one reference 3D image acquired of the back of each of thelivestock animals in the population of livestock animals, and wherein atleast one reference 3D image of the back of an identified animal isobtained at least every second week.
 2. The method according to claim 1,wherein said at least one reference 3D image of the back of anidentified animal is obtained at least once a week, or at least twice aweek, or at least three times a week, or at least every second day, orat least once a day, or at least twice a day.
 3. The method according toclaim 1, wherein said livestock animal is selected from the group ofcattle, cows, dairy cows, bulls, calves, pigs, sows, boars, castratedmales, piglets, horses, sheep, goats, deer, and/or wherein saidpopulation of livestock animals is a population of animals of the sametype, breed and/or race selected from the group of cattle, cows, dairycows, bulls, calves, pigs, sows, boars, castrated males, piglets,horses, sheep, goats, deer.
 4. The method according to claim 1, whereinthe extracted data and the reference data comprise values selected fromthe group of topographic profiles of the livestock animals.
 5. Themethod according to claim 4, wherein the topographic profiles areselected from the group of: the height of the animal, the broadness ofthe animal, contour line along the backbone of the animal, the length ofthe back, contour plots for different heights of the animal, volume ofthe animal above different heights of the animal, size of cavities,depth of cavities, the distance between two pre-selected points at theanimal, where said pre-selected points may be selected from the group ofright hip, left hip, right shoulder, left shoulder, tail head, neck,left forerib, left short rib start, left hook start, left hook anteriormidpoint; left hook, left hook posterior midpoint, left hook end, leftthurl, left pin, left tail head nadir, left tail head junction, tail,right tail head junction, right tail head nadir, right pin, right thurl,right hook end, right hook posterior midpoint, right hook, right hookanterior midpoint, right hook start, right short rib start.
 6. Themethod according to claim 1, wherein the extracted data and thereference data comprise at least one feature and/or at least one featurevector
 7. The method according to claim 6, wherein said at least onefeature and/or at least one feature vector relates to a characteristicfeature of the back of the animal.
 8. The method according to claim 1,wherein said at least one reference 3D image of a livestock animal isobtained by concurrently determining the identity of the livestockanimal by reading an identification marker attached to said livestockanimal.
 9. The method according to claim 1, wherein said 3D image and/orsaid reference 3D image is a topographic image of the back of thelivestock animals.
 10. The method according to claim 9, wherein saidtopographic image is a 3D image and/or multiple layers of 3D-images. 11.The method according to claim 1, wherein the extracted data and thereference data comprise at least one feature and/or at least one featurevector based on values of the area of multiple layers of said 3D-image.12. The method according to claim 1, wherein the extracted data and thereference data comprise at least one feature vector for preselecteddistances calculated from the distance from the ground or floorsupporting the livestock animals.
 13. The method according to claim 12,wherein said preselected distances are between 70 and 180 cm.
 14. Themethod according to claim 1, further comprising the step of determiningthe feed consumption of said identified preselected livestock animal.15. A system for determining the identity of an individual livestockanimal among a population of livestock animals with known identity, thesystem comprising: an imaging system configured for acquiring at leastone 3D image of the back of a preselected livestock animal; and aprocessing unit configured for: extracting data from said at least one3D image, said extracted data relating to the anatomy of the back and/ortopology of the back of the preselected livestock animal, and matchingsaid extracted data to reference data corresponding to the anatomy ofthe back and/or topology of the back of each of the livestock animalswith known identity, thereby identifying the preselected livestockanimal, wherein said reference data are extracted from at least onereference 3D image acquired of the back of each of the livestock animalsin the population of livestock animals, and wherein the system isconfigured such that at least one reference 3D image of the back of anidentified animal is obtained at least every second week.
 16. The systemaccording to claim 15, further comprising a reference imaging unit forproviding one or more reference 3D images of a livestock animal in thepopulation of livestock animals, said reference imaging unit comprising:at least one identity determining device configured to determine theidentity of said livestock animal; and at least one camera configured toacquire at least one 3D image of the back of said livestock animal,wherein the system is further configured to associate the determinedidentity of the livestock animal with said at least one 3D imageacquired by said camera(s) and optionally store said at least one 3Dimage as a reference 3D image.
 17. The system according to claim 16,wherein said at least one identity determining device is configured todetermine the identity of said livestock animal by reading at least oneidentification marker attached to said livestock animal.
 18. The systemaccording to claim 15, wherein said imaging system and/or said referenceimaging unit comprises one or more cameras selected from the group ofrange cameras, stereo cameras, time-of-flight cameras, and a 2D cameracomprising a depth sensor.
 19. The system according to claim 16, whereinsaid reference imaging unit is configured to acquire at least one 3Dimage of the back of a livestock animal, when the identity of saidlivestock animal has been determined by said at least one identitydetermining device
 20. The system according to claim 16, wherein saidreference imaging unit is configured to acquire at least one 3D image ofthe back of a livestock animal, and/or determine the identity of alivestock animal when said animal is within a predefined distance ofsaid identity determining device.
 21. The system according to claim 15,further comprising a feeding area imaging unit configured to acquireimages of a feeding area in front of the identified preselectedlivestock animal.